Quantifying the temporal dynamics of environmental awareness through longitudinal social media analysis

Understanding how societies perceive and adapt to environmental change requires analyzing awareness as a dynamic process emerging from coupled human-environment interactions. This study presents a longitudinal, data-driven analysis of environmental awareness based on three years of social-media discourse related to air pollution. Using sentiment analysis (Valence Aware Dictionary and Sentiment Reasoner and Twitter-RoBERTa) and topic modeling (BERTopic), we quantify the temporal evolution of collective public awareness and identify dominant discourse themes. A correlation analysis between sentiment indicators and PM10 levels reveals synchronized fluctuations, while mutual information indicates that public awareness becomes increasingly dependent on or shaped by pollution variability. Furthermore, Kruskal-Wallis tests confirm statistically significant temporal variations in sentiment distributions, underscoring adaptive shifts in public awareness across years. By interpreting awareness as a measurable order parameter within a complex socio-environmental system, this work demonstrates how collective perception emerges from nonlinear feedbacks between environmental forcing and public information flow. The proposed multilayer socio-environmental perception framework integrates machine-learning-based sentiment and topic analysis with complex-systems concepts to quantify emergent awareness dynamics from longitudinal social-media data.